ProLIP

Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.

A curated list of awesome prompt/adapter learning methods for vision-language models like CLIP.

This repository contains an easy and intuitive approach to few-shot NER using most similar expansion over spaCy embeddings. Now with entity scoring.

TEXTOIR is the first opensource toolkit for text open intent recognition. (ACL 2021)

This repository contains an easy and intuitive approach to few-shot classification using sentence-transformers or spaCy models, or zero-shot classification with Huggingface.

[ICCV'21] Official PyTorch implementation of Relational Embedding for Few-Shot Classification

Few-Shot Graph Classification via distance metric learning.

Repository of the ICNLSP 2024 paper "Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes"

[AAAI 2023] This is the code for our paper `Neighborhood-Regularized Self-Training for Learning with Few Labels'.

Use CLIP to create matching texts + embeddings for given images; useful for XAI, adversarial training